Risk management has been a topic of researchers for many years and is an important issue for business professionals. Risk management can help business professionals identify important adverse future events an entity, such as a firm, may face and help establish procedures to measure, mitigate and manage risk. For those investors extending credit to entities, risk management can help investors assess potential losses resulting from such activity. Likewise, for investors who hold an equity interest in entities, risk management can help investors assess potential volatility affecting those investments and adjust their portfolios accordingly.
Typically, a broad range of data sources are utilized in risk management. Many of these data sources are derived directly from public data sources including companies. For example, many researchers have developed credit risk models that rate companies based on their likelihood of defaulting on their debt or loans using financial accounting data, such as accounting ratios, and pricing data from pricing services, such as Moody's, S&P and Fitch. Example metrics computed in credit risk models include a probability of default (e.g., the likelihood that an entity will fail to meet its financial obligations), and loss given default (e.g., if a default occurs, the amount those who extended credit to the entity expect to lose).
While such sources of information provide valuable input to the credit risk modeling process, there is a vast amount of publicly available information that is overlooked by these models. For example, textual based data sources such as news articles that report on a firm's past, current, and possible future events typically include important information that is not considered in the credit risk modeling process. Further, the semantic context of text included in these data sources is typically not analyzed by these processes.
Accordingly, there is a need for improved credit risk modeling techniques that are capable of analyzing not only financial accounting ratios and pricing information, but also textual-based information.